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Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Author

Listed:
  • George Lãzãroiu

    (Spiru Haret University, Romania)

  • Armenia Androniceanu

    (The Bucharest University of Economic Studies, Romania)

  • Iulia Grecu

    (Spiru Haret University, Romania)

  • Gheorghe Grecu

    (Spiru Haret University, Romania)

  • Octav Neguri?ã

    (Spiru Haret University, Romania)

Abstract

Research background: With increasing evidence of cognitive technologies progressively integrating themselves at all levels of the manufacturing enterprises, there is an instrumental need for comprehending how cognitive manufacturing systems can provide increased value and precision in complex operational processes. Purpose of the article: In this research, prior findings were cumulated proving that cognitive manufacturing integrates artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production. Methods: Throughout April and June 2022, by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms including “cognitive Industrial Internet of Things”, “cognitive automation”, “cognitive manufacturing systems”, “cognitively-enhanced machine”, “cognitive technology-driven automation”, “cognitive computing technologies,” and “cognitive technologies.” The Systematic Review Data Repository (SRDR) was leveraged, a software program for the collecting, processing, and analysis of data for our research. The quality of the selected scholarly sources was evaluated by harnessing the Mixed Method Appraisal Tool (MMAT). AMSTAR (Assessing the Methodological Quality of Systematic Reviews) deployed artificial intelligence and intelligent workflows, and Dedoose was used for mixed methods research. VOSviewer layout algorithms and Dimensions bibliometric mapping served as data visualization tools. Findings & value added: Cognitive manufacturing systems is developed on sustainable product lifecycle management, Internet of Things-based real-time production logistics, and deep learning-assisted smart process planning, optimizing value creation capabilities and artificial intelligence-based decision-making algorithms. Subsequent interest should be oriented to how predictive maintenance can assist in cognitive manufacturing by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.

Suggested Citation

  • George Lãzãroiu & Armenia Androniceanu & Iulia Grecu & Gheorghe Grecu & Octav Neguri?ã, 2022. "Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing," Oeconomia Copernicana, Institute of Economic Research, vol. 13(4), pages 1047-1080, December.
    • Handle: RePEc:pes:ieroec:v:13:y:2022:i:4:p:1047-1080
    DOI: 10.24136/oc.2022.030
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    References listed on IDEAS

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    Cited by:

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    2. Peishu Chen & Yu He & Kai Yue & Guochang Fang, 2023. "Can Carbon Trading Promote Low-Carbon Transformation of High Energy Consumption Enterprises?—The Case of China," Energies, MDPI, vol. 16(8), pages 1-18, April.
    3. Cristina Bianca Pocol & Liana Stanca & Dan-Cristian Dabija & Veronica Câmpian & Sergiu Mișcoiu & Ioana Delia Pop, 2023. "A QCA Analysis of Knowledge Co-Creation Based on University–Industry Relationships," Mathematics, MDPI, vol. 11(2), pages 1-19, January.
    4. Elvira Nica & Gheorghe H. Popescu & Milos Poliak & Tomas Kliestik & Oana-Matilda Sabie, 2023. "Digital Twin Simulation Tools, Spatial Cognition Algorithms, and Multi-Sensor Fusion Technology in Sustainable Urban Governance Networks," Mathematics, MDPI, vol. 11(9), pages 1-25, April.
    5. Dan-Cristian Dabija & Luiela Magdalena Csorba & Florin-Lucian Isac & Sergiu Rusu, 2023. "Managing Sustainable Sharing Economy Platforms: A Stimulus–Organism–Response Based Structural Equation Modelling on an Emerging Market," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
    6. Balcerzak, Adam P. & Zinecker, Marek & Skalický, Roman & Rogalska, Elżbieta & Doubravský, Karel, 2023. "Technology-oriented start-ups and valuation: A novel approach based on specific contract terms," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    7. Domicián Máté & Ni Made Estiyanti & Adam Novotny, 2024. "How to support innovative small firms? Bibliometric analysis and visualization of start-up incubation," Journal of Innovation and Entrepreneurship, Springer, vol. 13(1), pages 1-26, December.

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    More about this item

    Keywords

    cognitive manufacturing; Artificial Intelligence of Things; cyber-physical system; big data-driven deep learning; real-time scheduling algorithm; smart device; sustainable product lifecycle management;
    All these keywords.

    JEL classification:

    • E42 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Monetary Sytsems; Standards; Regimes; Government and the Monetary System
    • J33 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Compensation Packages; Payment Methods
    • O14 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Industrialization; Manufacturing and Service Industries; Choice of Technology

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